Dynamic load curtailment system and method

ABSTRACT

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

This application is a continuation of U.S. patent application Ser. No.13/425,195 entitled “Dynamic Load Curtailment System And Method” filedMar. 20, 2012, which claims priority to Provisional Patent ApplicationNo. 61/496,422, entitled “System and Method of Controlling SetbackRecovery of a Power Consuming Device,” filed Jun. 13, 2011, andProvisional Patent Application No. 61/496,431, entitled “System andMethod of Controlling the Setback of a Power Consuming Device, filedJun. 13, 2011, the entire disclosures of which are incorporated hereinby reference.

This application includes material which is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present invention relates in general to the field of energymanagement, and in particular to dynamic load curtailment in an energymanagement system.

BACKGROUND

Load curtailment adjusts energy consumption down to a contextual targetthat is calculated by a load curtailment algorithm based on thehistorical consumption of a building. Dynamic load curtailment tunes thebuilding, constantly seeking the lowest loads possible. Over time, theload falls while the building finds its new equilibrium. Mostapplications that curtail loads operate on a schedule or thresholdbasis. These systems require a lot of tuning to get the building tooperate as efficiently as possible and even require retuning in somecases based on seasonal changes. If not tuned, the building either isn'tcurtailing the right amount of energy or is curtailing too much, whichresults in occupant disruption.

SUMMARY

The system and method of the invention utilizes an algorithm todynamically learn the optimum energy consumption operating condition fora building and monitor/control energy consuming equipment to keep thepeak demand to a minimum. This algorithm allows for buildings todynamically operate in the most efficient manner while being transparentto the building occupants.

In an embodiment, the dynamic demand limiting (load curtailment)algorithm employs two separate control schemes, one for HVAC loads andone for non-HVAC loads. Separate operating parameters can be applied tothe two types of loads and multiple (e.g., up to ten) non-HVAC controlzone loads can be configured.

The algorithm uses historical peak demand measurements in its real-timelimiting strategy. The algorithm continuously attempts to reduce peakdemand within the user configured parameters, such as minimum andmaximum temperature set points. When a new peak is inevitable, thealgorithm can strategically remove and/or introduce loads in a fashionthat limits the new peak magnitude and places the operating conditionswithin the user configured parameters. All curtailment actions arelogged within the energy management controller. These logs includerelevant data such as the date and time of curtailment and the load thatwas curtailed.

In an embodiment, the disclosed system and method utilizes an algorithmthat examines the previous seven days of metering information toidentify a peak interval in which it uses a percentage such as 95% ofthat peak interval as its target or a recent occupied average load inwhich it uses a percentage such as 105% of that average load as itstarget. The system then uses real-time load information to predict thedemand peak of the upcoming interval, and strategically curtailsassigned loads in order to limit the demand peak so as not to set a newpeak. In this manner, an automated tuning operation is created and thebuilding operates with improved efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments as illustrated in the accompanyingdrawings, in which reference characters refer to the same partsthroughout the various views. The drawings are not necessarily to scale,emphasis instead being placed upon illustrating principles of theinvention.

FIG. 1 shows a schematic block diagram illustrating an energy managementsystem for practicing the method of the invention.

FIG. 2 shows two charts illustrating an example of circuit lists andHVAC lists.

FIG. 3 shows a graph of demand over fifteen minute intervals toillustrate methods for finding peaks for calculations.

FIG. 4 shows a graph of demand over fifteen minute intervals toillustrate prediction of load over a time interval.

FIG. 5 shows a graph of demand over fifteen minute intervals toillustrate sending intervals into curtailment.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings.

FIG. 1 shows a schematic block diagram illustrating an exemplaryembodiment of an energy management system for practicing the method ofthe invention. A site controller with embedded control algorithmscontrols multiple electrical loads on circuits 1 through N via lightcontrol panels (LCPs). The site controller is typically wired to commonvoltages at an electrical distribution panel of a commercial orresidential building facility via a main line meter (power monitor). Thesite controller includes memory and a CPU for respectively storing andimplementing energy management algorithms in accordance with theinvention, discussed below. The algorithms accept real-time power andenvironmental variable measurements (including readings from thermostatsTStat 1 through TStat N) as inputs and determine how to control thepower delivered on the circuits 1 through N and to control set pointsand other configurable settings such as enabling/disabling compressorstages on TStat 1 through TStat N. The site controller may include apower supply and one or more wired or wireless local communication andcontrol interfaces for controlling Circuit 1 through Circuit N and TStat1 through TStat N. Thermostats TStat 1 through TStat N providetemperature and humidity inputs to the site controller, and outputcontrol signals to roof-top units RTU 1 through RTU N. A communicationinterface provides bi-directional communication with a communicationgateway, which in turn manages wired or wireless communications with aremote terminal.

One or more power monitors are coupled to the site controller either viawired or wireless connection. The power monitor includes hardware andfirmware to provide sampling functionality, including multipleanalog-to-digital converters for multi-channel fast waveform sampling ofinputs such as current and voltage. The power monitor includes wired orwireless communication interfaces, current and voltage monitoringinterfaces, memory, CPU, and may also include a power supply.

In an alternative embodiment, the A/D converters and voltage and currentmonitoring interfaces of the power monitor may be integrated within thesite controller to provide voltage and current monitoring without theuse of an external power monitor. Further, additional environmentalsensors, such as outdoor temperature sensors, may be monitored by thesystem. In yet another alternative embodiment, the control algorithmsmay be embedded and executed within the power monitor itself.

The current and voltage monitoring interfaces connect between the powercircuits being monitored and the A/D converter. Each channel may beconnected to a separate power circuit to monitor the flow of currentthrough the circuit. The connection is made with a current transformerat both a supply (i.e., hot) line and a return (i.e., neutral) line ofthe power circuit, which provides a waveform signal that isrepresentative of the current flow at the connection point.

FIG. 2 shows two charts illustrating an example of circuit configurationlists and HVAC configuration lists. Loads can be disabled by the systemin the order as listed therein, or as otherwise discussed in furtherdetail below. Separate lists are stored for HVAC loads and non-HVACloads.

FIG. 3 shows a graph illustrating demand over fifteen minute intervals.The graph line is exaggerated for clarity to show some distinct peaks,and the actual load line would be much flatter. A dynamic loadcurtailment algorithm constantly monitors the last seven days for thepeak energy usage (in kw). If you imagine this graph in motion, it willconstantly move to the left. At the point in time at which this chartwas generated, the peak is at the end of day-4. Each day, that peak willmove to the left, to the end of day-5, then -6, then it will “fall off”the graph to the left. In the mean time, the “new peak” (at the end ofday-2) moves to the left also. When the “old peak” falls off the graphto the left, the system uses the “new peak.” Eventually, that new peakwill fall off the graph to the left and be replaced with a new one, andso on. The number of days the algorithm uses to determine the peakenergy usage is configurable and can be more or less than seven.Moreover, the billing intervals are configurable and can set to more orless than 15 minutes. The next FIG. shows what happens to the targetload when the “old peak” falls off the left side of this chart and a“new peak” appears.

With reference to FIG. 4, at the same time that the system is looking atthe last seven days for the peak, it is also predicting the load for thenext billing interval, which is typically 15 minutes. The controller hasenough information available to it to make the prediction accurately forthe average load in the next 15 minutes. The load is the average for theinterval, not any one single point in the load. Thus, while the realtime load bounces around the predicted load, the algorithm isn'tconcerned with outlying dips and spikes, only in a way to drive the loaddown for the billing interval.

FIG. 5 shows a graph of demand over fifteen minute intervals toillustrate sending intervals into curtailment. In this graph, twointervals have a predicted load over the target load. Those intervalsbegin curtailment depending on configuration of the system.

The actions used to curtail load are (a) de-energizing of circuits and(h) reduction of consumption of HVAC within comfort limits. Tode-energize circuits, the system turns off predefined circuits withinpredefined limits. These are “nice to have” circuits, like spotlights onthe exterior of building or a wall heater. Reduce consumption of HVACwithin comfort limits by either turning off stage two (one of twocompressors in the RTU) or tuning the set point to reduce cycles.

In accordance with the invention, total facility load is monitored, forexample via the main load power monitor, and predictions for totalfacility demand are calculated and compared to a target value for totalfacility KW demand. The demand interval should be configured as that ofthe customer's billing tariff. Projected load is determined based onreal-time load information and other information such as typicalequipment run-times and equipment operational schedules. When projecteddemand indicates that the demand target will be exceeded for the currentinterval, set points or active stages for individual HVAC units areincrementally changed in an effort to reduce the associated load asneeded to keep the total facility demand from exceeding the targetlimit. Control zones can also be de-activated in order to keep thetarget demand from being exceeded.

HVAC units can be curtailed by either (a) turning off only stage 2 or(b) increasing or decreasing the cooling or heating set point,respectively, by several degrees (configurable). The curtailment optionto be applied for a particular HVAC unit is configurable, along with thecurtailment priority order. Curtailment priority order can be configuredto rotate to ensure some runtime for each unit. In addition to theminimum on-time and off-time requirements enforced by the controllingthermostats, thermostat on and off time requirements are enforced bythis control scheme as well.

Control zones can be curtailed by de-energizing their circuits for aconfigurable amount of time during the demand interval. This ensuresthat these loads will always run for a minimal amount of time. Allcontrol zones are the lowest priority in this control scheme and thuswill be the first loads to be curtailed. Once all available controlzones have been curtailed, only then would HVAC units be altered inaccordance with configured run times. Only one load will be changed(control zone and/or HVAC units) per minute. Alternatively, other timeintervals for changing loads could be configured.

Curtailment by Turning Off Only Stage 2

This control scheme is not synchronized with the demand interval. Thesequence of operation is as follows:

-   -   1) At any time during the demand interval, these HVAC loads are        curtailed in a prioritized “staggered” fashion (one at a time        per minute) by immediately turning off only stage 2 when the        following conditions occur:        -   a) Predicted total facility demand exceeds the target total            facility KW value;        -   b) Zone temperature does not exceed the normal target zone            temperature plus the predefined curtailment offset unless            the system is in recovery in which case the zone temperature            will not exceed unoccupied settings;        -   c) Minimum configured thermostat on time has been satisfied.    -   2) Once an HVAC load has been curtailed, it will continue to be        curtailed for at least the configured thermostat off time        (regardless of zone temperature).    -   3) These HVAC loads will remain in curtailment, after the        configured thermostat off time, as long as the predicted total        facility demand exceeds the target total facility KW value        (regardless of zone temperature).    -   4) When predicted total facility demand becomes less than the        target total facility KW value, stage 2 of these units is        re-enabled in a prioritized “staggered” fashion—if the        thermostat off time has been satisfied.

Curtailment by Changing Set Points

This control scheme is not synchronized with the demand interval. Thesequence of operation is as follows:

-   -   1) At any time during the demand interval, these HVAC loads will        be curtailed in a prioritized “staggered” fashion (one at a time        per minute) by immediately increasing or decreasing the cooling        or heating set point, respectively, by the number of degrees        equal to the curtailment offset (configurable per unit) unless        the system is in recovery in which case the thermostat will be        set to unoccupied settings when the following conditions occur:        -   a) Predicted total facility demand exceeds the target total            facility KW value;        -   b) Minimum thermostat on time has been satisfied.    -   2) Once an HVAC load has been curtailed, it will continue to be        curtailed for at least the configured thermostat off time        (regardless of zone temperature).    -   3) The HVAC load will remain in curtailment, after the        configured thermostat off time, as long as the predicted total        facility demand exceeds the target total facility KW value.    -   4) When predicted total facility demand becomes less than the        target total facility KW value, these HVAC units are returned to        normal set point values in a prioritized “staggered” fashion—if        the configured thermostat off time has been satisfied.

Curtailment by De-Energizing Loads

This form of curtailment cuts off or decreases power to the loads. Thesequence of operation is as follows:

-   -   1) At the beginning of each demand interval, these loads are        enabled to run for a predefined minimum runtime (per interval).    -   2) After the minimum runtime has been satisfied, these loads are        immediately disabled for the remainder of the interval if the        predicted total facility demand exceeds the target facility KW        value. The loads will be disabled in the order as listed in the        configuration.

Determination of Total Facility Demand Target

The total facility demand target can be determined by either (a) apredefined table of monthly target values (12 values—one for eachcalendar month), or (b) a dynamically calculated target value. Withdynamic calculation of total facility target KW, controllers willautomatically and dynamically calculate the target total facility KWvalue such that the target total facility KW is the greater of:

1. 0.95×Recent Total Facility Demand Peak where Recent Total FacilityDemand Peak=the highest total facility demand value for the previousseven (7) days (determined dynamically).

2. 1.05×Recent Occupied Average Total Facility Load, where RecentOccupied Average Total Facility Load=the average value of total facilityload during occupied hours for the previous seven (7) days (determineddynamically). Alternatively, either of Recent Total Facility Demand Peakor Recent Occupied Average Total Facility Load can be used alone tocalculate the target total facility KW value. Moreover, a configurablenumber of previous days can be used to derive these quantities.

Target total facility KW is calculated at the beginning of each demandinterval. The goal is to achieve a stable control scheme thatautomatically and gradually adjusts to the best achievable totalfacility demand deduction as total facility load characteristics varyover time.

The 0.95 and 1.05 factors (above) on the dynamic calculations areconfigurable and may need to be adjusted to achieve optimum stabledemand reduction. The lower the setting, the faster the system willreact. The higher the setting, the slower the system will react.

The present invention has been described above with reference toalgorithms and operational illustrations of methods and devices todynamically curtail load in an energy management system. It isunderstood that each step of the algorithm or operational illustrationsmay be implemented by means of analog or digital hardware and computerprogram instructions. These computer program instructions may be storedon computer-readable media and provided to a processor of a generalpurpose computer, special purpose computer, ASIC, or other programmabledata processing apparatus, such that the instructions, which execute viathe processor of the computer or other programmable data processingapparatus, implement the functions/acts specified. Examples ofcomputer-readable media include but are not limited to recordable andnon-recordable type media such as volatile and non-volatile memorydevices, read only memory (ROM), random access memory (RAM), flashmemory devices, floppy and other removable disks, magnetic disk storagemedia, optical storage media (e.g., Compact Disk Read-Only Memory (CDROMS), Digital Versatile Disks (DVDs), etc.), among others. In somealternate implementations, the steps may occur out of the order noted inthe operational illustrations.

The load curtailment algorithms taught above may operate in combinationwith other energy management algorithms, including algorithms for HVACrecovery, HVAC setback, humidity control, and demand controlventilation. Such algorithms tune buildings to reduce power consumptionwhile considering comfort. The algorithms may run on the site controllerillustrated in FIG. 1 or in the power monitor, and discussed above. Thealgorithms may be configured to work together as follows. The loadcurtailment algorithm described above adjusts consumption down tocontextual target that it calculates based on the historical consumptionof the building. An HVAC Recovery algorithm efficiently returnsbuildings to occupied temperature settings, as disclosed in ProvisionalPatent Application No. 61/496,422, entitled “System and Method ofControlling Setback Recovery of a Power Consuming Device,” filed Jun.13, 2011, and expressly incorporated herein in its entirety. An HVACSetback algorithm efficiently returns buildings to unoccupiedtemperature settings, as disclosed in Provisional Patent Application No.61/496,431, entitled “System and Method of Controlling the Setback of aPower Consuming Device, filed Jun. 13, 2011, and expressly incorporatedherein in its entirety. A Humidity Control algorithm uses HVAC systemsto remove humidity from indoor air. A Demand Control Ventilation (DCV)algorithm draws external air into the system to affect air quality.Because the algorithms run on the same controller, the system canprevent conflicts between working algorithms by choosing the most energyefficient output of the algorithms. Each algorithm has its own comfortlimits, so the most energy efficient output already considers comfort ofa building's occupants.

While the invention has been particularly shown and described withreference to a preferred embodiment thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A method of load curtailment for a facility,comprising: examining a number of previous days of metering informationto identify a demand peak target; utilizing real-time load informationto predict the demand peak of the upcoming interval; and, strategicallycurtailing assigned loads in order to limit the demand peak so as not toset a new peak; whereby an automated tuning operation is created and thebuilding operates with improved efficiency.
 2. The method of loadcurtailment in accordance with claim 1, wherein the examining stepcomprises: calculating a target total facility KW value such that thetarget total facility KW is the greater of: a first proportion timesrecent total facility demand peak, where recent total facility demandpeak is equal to the highest total facility demand value for the numberof previous days, and a second proportion times recent occupied averagetotal facility load, where recent occupied average total facility loadis equal to the average value of total facility load during occupiedhours for the number of previous days.
 3. The method of load curtailmentin accordance with claim 2, wherein the first proportion is 0.95.
 4. Themethod of load curtailment in accordance with claim 2, wherein thesecond proportion is 1.05.
 5. The method of load curtailment inaccordance with claim 2, wherein the first and second proportions areconfigurable.
 6. The method of load curtailment in accordance with claim1, wherein the number of previous days of metering information isconfigurable.
 7. The method of load curtailment in accordance with claim1, wherein the number of previous days of metering information that isexamined is seven.
 8. A method for dynamically learning the optimumenergy consumption operating condition for a building and monitoring andcontrolling energy consuming equipment to keep the peak demand intervalat a minimum, comprising: utilizing a dynamic demand-limiting algorithmthat employs at least a first control scheme for HVAC loads and asecond, separate control scheme for non-HVAC loads; applying separateoperating parameters to the HVAC loads and the non-HVAC loads; and,using historical peak demand measurements in a real-time limitingstrategy to continuously attempt to reduce peak demand within the userconfigured parameters.
 9. The method according to claim 8, wherein whena new peak is inevitable, loads are introduced in a fashion that limitsa new peak magnitude and places the operating conditions within userconfigured parameters.
 10. Computer program process code, tangiblystored on at least one non-transitory computer readable medium, thecomputer program process code comprising instruction implementing amethod for using a computing device to perform dynamic load curtailment,comprising instructions for: examining a number of previous days ofmetering information to identify a demand peak target; utilizingreal-time load information to predict the demand peak of the upcominginterval; and, strategically curtailing assigned loads in order to limitthe demand peak so as not to set a new peak; whereby an automated tuningoperation is created and the building operates with improved efficiency.